Improved dropClust R package with integrative analysis support for scRNA-seq data

Self-assembly of differentiated dental pulp stem cells facilitates spheroid human dental organoid formation and prevascularization

BACKGROUND

The self-assembly of solid organs from stem cells has the potential to greatly expand the applicability of regenerative medicine. Stem cells can self-organise into microsized organ units, partially modelling tissue function and regeneration. Dental pulp organoids have been used to recapitulate the processes of tooth development and related diseases. However, the lack of vasculature limits the utility of dental pulp organoids.

AIM

To improve survival and aid in recovery after stem cell transplantation, we demonstrated the three-dimensional (3D) self-assembly of adult stem cell-human dental pulp stem cells (hDPSCs) and endothelial cells (ECs) into a novel type of spheroid-shaped dental pulp organoid in vitro under hypoxia and conditioned medium (CM).

METHODS

During culture, primary hDPSCs were induced to differentiate into ECs by exposing them to a hypoxic environment and CM. The hypoxic pretreated hDPSCs were then mixed with ECs at specific ratios and conditioned in a 3D environment to produce prevascularized dental pulp organoids. The biological characteristics of the organoids were analysed, and the regulatory pathways associated with angiogenesis were studied.

RESULTS

The combination of these two agents resulted in prevascularized human dental pulp organoids (Vorganoids) that more closely resembled dental pulp tissue in terms of morphology and function. Single-cell RNA sequencing of dental pulp tissue and RNA sequencing of Vorganoids were integrated to analyse key regulatory pathways associated with angiogenesis. The biomarkers forkhead box protein O1 and fibroblast growth factor 2 were identified to be involved in the regulation of Vorganoids.

CONCLUSION

In this innovative study, we effectively established an in vitro model of Vorganoids and used it to elucidate new mechanisms of angiogenesis during regeneration, facilitating the development of clinical treatment strategies.

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.